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llama.cpp
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Reformat report layout

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Ed Addario 2025-08-03 00:51:33 +01:00
parent 4c01f51ae1
commit a32a2ecbed
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1 changed files with 25 additions and 17 deletions
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42
tools/imatrix/imatrix.cpp
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@ -1219,25 +1219,23 @@ static bool show_statistics(const common_params & params) {
std::map<int, weighted_stats> ws; std::map<int, weighted_stats> ws;
LOG_INF("\nComputing statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast<int>(ts.size())); LOG_INF("\nComputing statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast<int>(ts.size()));
LOG_INF( LOG_INF("\n%6s\t%18s\t%13s\t%8s\t%8s\t%7s\t%15s\t%13s\t%12s\t%s\t%5s\t%10s\n",
"\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n", "Layer",
" Layer", "Tensor",
" Tensor", tensor_calc_mode == 1 ? "L₂ Norm" : "Σ(Act²)",
tensor_calc_mode == 1 ? " L₂ Norm" : " Σ(Act²)", "Min",
" Min", "Max",
" Max", "μ",
" μ", "σ",
" σ",
" % Active",
"N", "N",
" Entropy", "Entropy",
"E (norm)", "E (norm)",
"ZD", "ZD",
" CosSim" "CosSim"
); );
LOG_INF( LOG_INF(
"==============================================================================================================" "=============================================================================================================="
"===========================================================\n"); "=============================================================\n");
for (const auto & tstat : ts) { for (const auto & tstat : ts) {
std::string layer, name; std::string layer, name;
process_tensor_name(tstat.tensor, layer, name); process_tensor_name(tstat.tensor, layer, name);
@ -1249,10 +1247,20 @@ static bool show_statistics(const common_params & params) {
blk = -1; // not a block layer blk = -1; // not a block layer
} }
LOG_INF("%5s\t%-20s\t%10.2f\t%8.4f\t%11.4f\t%6.2f\t%6.2f\t%8.2f%%\t%6d\t%10.4f\t%6.2f%%\t%10.2f%%\t%8.4f\n", LOG_INF("%5s\t%-20s\t%11.2f\t%10.4f\t%10.4f\t%8.2f\t%8.2f\t%7d\t%12.4f\t%7.2f%%\t%6.2f%%\t%10.4f\n",
layer.c_str(), name.c_str(), tstat.sum_values, tstat.min_values, tstat.max_values, tstat.mean_values, layer.c_str(),
tstat.stddev, tstat.active * 100.0f, tstat.elements, tstat.entropy, name.c_str(),
100.0f * (tstat.entropy / std::log2(tstat.elements)), 100.0f * tstat.zd_score, tstat.cossim); tstat.sum_values,
tstat.min_values,
tstat.max_values,
tstat.mean_values,
tstat.stddev,
tstat.elements,
tstat.entropy,
100.0f * (tstat.entropy / std::log2(tstat.elements)),
100.0f * tstat.zd_score,
tstat.cossim
);
const float weighted_bias = tstat.elements * tstat.sum_values; const float weighted_bias = tstat.elements * tstat.sum_values;
const float weighted_zd = tstat.elements * tstat.zd_score; const float weighted_zd = tstat.elements * tstat.zd_score;